Over the past few years, ride-sharing has emerged as an e ective way to relieve tra c congestion. A key problem for these platforms is to come up with a revenue-optimal (or GMV-optimal) pricing scheme and an induced vehicle dispatching policy that incorporate geographic and temporal information. In this paper, we aim to tackle this problem via an economic approach. Modeled naively, the underlyi...

This paper examines an integrated decision-making process regarding pricing for uncertain demand and sourcing from uncertain supply, which are often studied separately in the literature. Our analysis of the integrated system suggests that the base stock list price policy fails to achieve optimality even under deterministic demand. Instead, the optimal policy is characterized by two critical val...

We study the welfare implications of scalable price targeting, an extreme form of third-degree price discrimination implemented with machine learning for a large, digital firm. Targeted prices are computed by solving the firm’s Bayesian Decision-Theoretic pricing problem based on a database with a high-dimensional vector of customer features that are observed prior to the price quote. To identi...

Most companies prefer to use absorption costing rule rather than marginal cost pricing. This article is aimed at defining the absorption costing rule as deriving from a principal-agent formulation of two tier organizations : (i) the upstream unit fixes the production capacity and uses it as a cost driver to compute the average cost (ii) the downstream unit operates on the market and chooses the...

We characterize optimal state-dependent pricing rules under various forms of infrequent information. In all models, infrequent price changes arise from the existence of a lump-sum “menu cost.” We entertain various alternatives for the source and nature of infrequent information. In two benchmark cases with continuously available information, optimal pricing rules are purely state-dependent. In ...

This paper studies various approaches to demand learning in the context of a one-shot inventory replenishment problem with dynamic pricing. The customer arrival process is assumed to be piecewise deterministic and Markovian with an unknown parameter. Homogeneous customers have an iso-elastic demand function and do not behave strategically. We study full information, non-learning, passive learni...

BACKGROUND It is essential to allocate available resources equitably in order to ensure accessibility and affordability of essential medicines, especially in less fortunate nations with limited health funding. Currently, transparent and evidence based research is required to evaluate decision making regarding drug registration, drug pricing and reimbursement processes in Mongolia. OBJECTIVE T...

Resources including various assets of supply chains, face random demand over time and can be shared by others. We consider an operational setting where a resource is shared by two different classes of customers, the primary class (existing customers) and the secondary class (new firms) customers, under a service level based pricing contract with the secondary class customers which specifies the...

Carbon emission trade is one of the most common carbon emission regulation policies, manufactures must take measures to cope with this new situation. This paper dedicated to design a decision support system (DSS) based on our proposed model. We examined the manufacture’s joint emission reduction effort and pricing decisions with carbon emission trade. We formulate this problem as a stackelberg ...

This study aims to analyze the effect of tax planning, tunnelling incentive, debt covenant, and intangible asset on decision transfer pricing in mining companies listed Indonesian Stock Exchange 2017 – 2019. research uses quantitative methods with secondary data from company financial annual reports. The hypothesis testing used is panel random model. Based result analysis, it can be concluded t...